Poor data quality can be a vast and costly problem for entities that produce, transform, and leverage various types of data for both internal purposes and for associated customers. As the quantity of data produced and transmitted over communication networks continue to grow exponentially, manual data quality detection methods (e.g., inspection) often fails to discover underlying quality issues. While attempts have been made to implement automated data quality detection systems, automated solutions typically rely on tool-specific, proprietary formats and processes that are limited in their portability across disparate domains and are often complicated to design and administer.